# Events at Physics

<< Spring 2021 Summer 2021 Fall 2021 >>
Subscribe your calendar or receive email announcements of events

### Monday, July 12th, 2021

No events scheduled

### Tuesday, July 13th, 2021

Network in Neutrinos, Nuclear Astrophysics, and Symmetries (N3AS) Seminar
Neutrino gravitational wave memory from a core-collapse supernova
Time: 2:00 pm
Place: https://berkeley.zoom.us/j/91922781599
Speaker: Mainak Mukhopadhyay, Arizona State University
Abstract: General Relativity predicts that the passage of matter or radiation from an asymmetrically-emitting source should cause a permanent change in the local space-time metric. This phenomenon, called the \emph{gravitational memory effect}, has never been observed, however supernova neutrinos have long been considered a promising avenue for its detection in the future. With the advent of deci-Hertz gravitational wave interferometers, observing the supernova neutrino memory will be possible, with important implications for multimessenger astronomy and for tests of gravity. In this work, we develop a phenomenological (analytical) toy model for the supernova neutrino memory effect, which is overall consistent with the results of numerical simulations. This description is then generalized to several case studies of interest. We find that, for a galactic supernova, the dimensionless strain, $h(t)$, is of order $\sim 10^{-22} - 10^{-21}$, and develops over a typical time scale that varies between $\sim 0.1 - 10$ s, depending on the time-evolution of the anisotropy of the neutrino emission. The characteristic strain, $h_c(f)$, has a maximum at a frequency $f_{max} \sim {\mathcal O}(10^{-1}) - {\mathcal O}(1)$ Hz. The detailed features of the time- and frequency-structure of the memory strain will inform us of the matter dynamics near the collapsed core, and allow to distinguish between different stellar collapse scenarios. Next generation gravitational wave detectors like DECIGO and BBO will be sensitive to the neutrino memory effect for supernovae at typical galactic distances and beyond; with Ultimate DECIGO exceeding a detectability distance of 10 Mpc.
Host: Baha Balantekin

### Wednesday, July 14th, 2021

Physics ∩ ML Seminar
Learning Differential Equations
Time: 11:00 am
Place: Online Seminar: Please sign up for our mailing list at www.physicsmeetsml.org for zoom link
Speaker: Jesse Bettencourt, University of Toronto
Abstract: Differential equations provide a natural and productive language to describe and manipulate physical systems. As well, the interdisciplinary literature developed toward the study of differential equations is rich with conceptual and technical results. I will discuss the integration of these methods with Machine Learning. I will introduce Neural Ordinary Differential Equations, a class of initial value problems whose dynamics are specified by a neural network. I will describe some methods for learning the differential equation via gradient optimization. I will highlight some areas where this treatment is both conceptually elegant and practically effective. In particular, I will discuss Continuous Normalizing Flows for density estimation and an extension (FFJORD) that demonstrates performance improvement through numerical approximation. I will also discuss recent work to regularize learned differential equations such that their solution can be efficiently approximated by a numerical solver. I will describe recent advances in (Higher-Order) Automatic Differentiation that facilitate these methods and may be a useful tool for future techniques to study the interface of physics and Machine Learning.

### Thursday, July 15th, 2021

Cosmology Journal Club
Time: 12:00 pm
Place:
Abstract: Each week, we start with a couple scheduled talks about one's research, or an arXiv paper. Then we open up to the group for anyone to discuss an arXiv paper.

All are welcome and all fields of cosmology are appropriate.

Contact Ross Cawthon, cawthon@wisc, for more information.

Zoom info
Meeting ID: 93592708053, passcode: cmbadger

Or click: